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“Machine Learning” is the process of enabling computing devices to try, learn, do and verify assigned tasks to be performed on their own without being hard-coded to do so. The learning process in the code needs to evolve on its own with the changing parameters and perform accordingly. Earlier on human used to analyze all the scenarios in a task and we would dictate the steps required to the computer but during wider and more complex situations we realized it’s better for the computers to develop its own algorithm.

This domain of Machine Learning implements various tracks to help computers automatedly accomplish tasks where no correct sequence of steps is known. It involves training the computer through a vast number of situations and label them as successful or not and accordingly perform the correct sequence of action for the respective situation. This is called training data which is used to improve the effectiveness and efficiency of an algorithm. We have extensively used this procedure to achieve significant results in regard to our efficiency in predicting the choice of a consumer and also establishing a brain map for like/dislike.